Optimal Regularization Can Mitigate Double Descent

4 March 2020

Papers citing "Optimal Regularization Can Mitigate Double Descent"

28 / 28 papers shown

Title
The Double Descent Behavior in Two Layer Neural Network for Binary Classification Chathurika S Abeykoon A. Beknazaryan Hailin Sang 51 1 0 27 Apr 2025
How more data can hurt: Instability and regularization in next-generation reservoir computing Yuanzhao Zhang Edmilson Roque dos Santos Sean P. Cornelius 77 2 0 28 Jan 2025
Learning Linear Dynamics from Bilinear Observations Yahya Sattar Yassir Jedra Sarah Dean 26 1 0 24 Sep 2024
Information-Theoretic Progress Measures reveal Grokking is an Emergent Phase Transition Kenzo Clauw S. Stramaglia Daniele Marinazzo 50 3 0 16 Aug 2024
Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis Yufan Li Subhabrata Sen Ben Adlam MLT 45 1 0 18 Apr 2024
Generalized equivalences between subsampling and ridge regularization Pratik V. Patil Jin-Hong Du 24 5 0 29 May 2023
DSD $^2$ : Can We Dodge Sparse Double Descent and Compress the Neural Network Worry-Free? Victor Quétu Enzo Tartaglione 24 7 0 02 Mar 2023
Can we avoid Double Descent in Deep Neural Networks? Victor Quétu Enzo Tartaglione AI4CE 20 3 0 26 Feb 2023
Cliff-Learning T. T. Wang I. Zablotchi Nir Shavit Jonathan S. Rosenfeld 26 0 0 14 Feb 2023
Gradient flow in the gaussian covariate model: exact solution of learning curves and multiple descent structures Antione Bodin N. Macris 34 4 0 13 Dec 2022
A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance Marco Loog T. Viering 21 1 0 25 Nov 2022
A Solvable Model of Neural Scaling Laws A. Maloney Daniel A. Roberts J. Sully 31 51 0 30 Oct 2022
Global Convergence of SGD On Two Layer Neural Nets Pulkit Gopalani Anirbit Mukherjee 23 5 0 20 Oct 2022
On the Impossible Safety of Large AI Models El-Mahdi El-Mhamdi Sadegh Farhadkhani R. Guerraoui Nirupam Gupta L. Hoang Rafael Pinot Sébastien Rouault John Stephan 30 31 0 30 Sep 2022
Information FOMO: The unhealthy fear of missing out on information. A method for removing misleading data for healthier models Ethan Pickering T. Sapsis 16 6 0 27 Aug 2022
Regularization-wise double descent: Why it occurs and how to eliminate it Fatih Yilmaz Reinhard Heckel 25 11 0 03 Jun 2022
Estimation under Model Misspecification with Fake Features Martin Hellkvist Ayça Özçelikkale Anders Ahlén 14 11 0 07 Mar 2022
Learning Curves for Decision Making in Supervised Machine Learning: A Survey F. Mohr Jan N. van Rijn 38 52 0 28 Jan 2022
A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning Yehuda Dar Vidya Muthukumar Richard G. Baraniuk 29 71 0 06 Sep 2021
Interpolation can hurt robust generalization even when there is no noise Konstantin Donhauser Alexandru cTifrea Michael Aerni Reinhard Heckel Fanny Yang 31 14 0 05 Aug 2021
Double Descent and Other Interpolation Phenomena in GANs Lorenzo Luzi Yehuda Dar Richard Baraniuk 18 5 0 07 Jun 2021
Towards an Understanding of Benign Overfitting in Neural Networks Zhu Li Zhi-Hua Zhou A. Gretton MLT 33 35 0 06 Jun 2021
The Shape of Learning Curves: a Review T. Viering Marco Loog 18 122 0 19 Mar 2021
Low Curvature Activations Reduce Overfitting in Adversarial Training Vasu Singla Sahil Singla David Jacobs S. Feizi AAML 32 45 0 15 Feb 2021
Multiple Descent: Design Your Own Generalization Curve Lin Chen Yifei Min M. Belkin Amin Karbasi DRL 18 61 0 03 Aug 2020
Shape Matters: Understanding the Implicit Bias of the Noise Covariance Jeff Z. HaoChen Colin Wei J. Lee Tengyu Ma 23 93 0 15 Jun 2020
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond Fanghui Liu Xiaolin Huang Yudong Chen Johan A. K. Suykens BDL 34 172 0 23 Apr 2020
Bayesian Deep Learning and a Probabilistic Perspective of Generalization A. Wilson Pavel Izmailov UQCV BDL OOD 19 639 0 20 Feb 2020